{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Self-tuning random forests\n",
    "\n",
    "A demonstration of ensembling and tuning in MLJ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Warning: `getindex(o::PyObject, s::AbstractString)` is deprecated in favor of dot overloading (`getproperty`) so elements should now be accessed as e.g. `o.\"s\"` instead of `o[\"s\"]`.\n",
      "│   caller = top-level scope at none:0\n",
      "└ @ Core none:0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Plots.PyPlotBackend()"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# uncomment 2 lines for parallelized ensemble building:\n",
    "# using Distributed\n",
    "# addprocs()\n",
    "\n",
    "using Suppressor\n",
    "using MLJ\n",
    "using Plots\n",
    "pyplot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load a task (data plus learning objective):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0m\u001b[1mSupervisedTask @ 1…76\u001b[22m\n"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "task = load_boston()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which models are available for this task?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dict{String,Any} with 4 entries:\n",
       "  \"MLJ\"          => Any[\"KNNRegressor\", \"RidgeRegressor\"]\n",
       "  \"DecisionTree\" => Any[\"DecisionTreeRegressor\"]\n",
       "  \"ScikitLearn\"  => Any[\"SVMRegressor\", \"SVMLRegressor\", \"SVMNuRegressor\"]\n",
       "  \"XGBoost\"      => Any[\"XGBoostRegressor\"]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models(task)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluating a single decision tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "import MLJModels ✔\n",
      "import DecisionTree ✔\n",
      "import MLJModels.DecisionTree_.DecisionTreeRegressor ✔\n"
     ]
    }
   ],
   "source": [
    "@load DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Evaluating using a holdout set. \n",
      "│ fraction_train=0.8 \n",
      "│ shuffle=false \n",
      "│ measure=Function[rms, rmslp1] \n",
      "│ operation=StatsBase.predict \n",
      "│ Resampling from all rows. \n",
      "└ @ MLJ /Users/anthony/Dropbox/Julia7/MLJ/MLJ/src/resampling.jl:91\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(MLJ.rms = 7.058450107747526,\n",
       " MLJ.rmslp1 = 0.3275559703714572,)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree = DecisionTreeRegressor()\n",
    "mach = machine(tree, task)\n",
    "evaluate!(mach, resampling=Holdout(fraction_train=0.8), measure=[rms,rmslp1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a random forest by ensembling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLJ.DeterministicEnsembleModel(atom = \u001b[0m\u001b[1mDecisionTreeRegressor @ 3…77\u001b[22m,\n",
       "                               weights = Float64[],\n",
       "                               bagging_fraction = 0.8,\n",
       "                               rng_seed = 0,\n",
       "                               n = 100,\n",
       "                               parallel = true,)\u001b[0m\u001b[1m @ 8…63\u001b[22m"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest = EnsembleModel(atom=tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLJ.DeterministicEnsembleModel(atom = DecisionTreeRegressor(pruning_purity_threshold = 0.0,\n",
      "                                                            max_depth = -1,\n",
      "                                                            min_samples_leaf = 5,\n",
      "                                                            min_samples_split = 2,\n",
      "                                                            min_purity_increase = 0.0,\n",
      "                                                            n_subfeatures = 0,\n",
      "                                                            post_prune = false,),\n",
      "                               weights = Float64[],\n",
      "                               bagging_fraction = 0.8,\n",
      "                               rng_seed = 0,\n",
      "                               n = 100,\n",
      "                               parallel = true,)\u001b[0m\u001b[1m @ 8…63\u001b[22m"
     ]
    }
   ],
   "source": [
    "@more"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by reducing number of features sampled at each tree node. We will use the square root of the\n",
    "number of features, a common default:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree.n_subfeatures = 3;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get an idea of how many trees we need, we wrap our forest model in the task, pick a range for the ensemeble size, `n`, and generate learning curves (plots of model performance against `n`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mach = machine(forest, task)\n",
    "r = range(forest, :n, lower=10, upper=1000)\n",
    "curves = learning_curve!(mach,\n",
    "    resampling=Holdout(fraction_train=0.8),\n",
    "    nested_range=(n=r,), \n",
    "    measure=rms, n=4)\n",
    "@suppress plot(curves.parameter_values, curves.measurements,\n",
    "     xlab=\"number of trees\", ylab=\"rms \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "savefig(\"learningcurves.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "forest.n = 300;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As `forest` is a composite model, it has nested hyperparameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(atom = (pruning_purity_threshold = 0.0,\n",
       "         max_depth = -1,\n",
       "         min_samples_leaf = 5,\n",
       "         min_samples_split = 2,\n",
       "         min_purity_increase = 0.0,\n",
       "         n_subfeatures = 3,\n",
       "         post_prune = false,),\n",
       " weights = Float64[],\n",
       " bagging_fraction = 0.8,\n",
       " rng_seed = 0,\n",
       " n = 300,\n",
       " parallel = true,)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params(forest) # all hyperparameters, as a named tuple"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's define ranges for two hyperparameters we want to tune:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "r1 = range(tree, :n_subfeatures, lower=1, upper=12)\n",
    "r2 = range(forest, :bagging_fraction, lower=0.4, upper=1.0);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We collate the ranges using by matching the pattern of `params(forest)` above, omitting parameters that don't change:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(atom = (n_subfeatures = \u001b[0m\u001b[1mNumericRange{n_subfeatures} @ 4…31\u001b[22m,),\n",
       " bagging_fraction = \u001b[0m\u001b[1mNumericRange{bagging_fraction} @ 3…94\u001b[22m,)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nested_ranges = (atom=(n_subfeatures=r1,), \n",
    "                 bagging_fraction=r2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now wrap our forest in tuning strategy to obtain a new \"self-tuning\" model!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLJ.DeterministicTunedModel(model = \u001b[0m\u001b[1mDeterministicEnsembleModel @ 8…63\u001b[22m,\n",
       "                            tuning = \u001b[0m\u001b[1mGrid @ 1…10\u001b[22m,\n",
       "                            resampling = \u001b[0m\u001b[1mCV @ 6…49\u001b[22m,\n",
       "                            measure = MLJ.rms,\n",
       "                            operation = StatsBase.predict,\n",
       "                            nested_ranges = (atom = (n_subfeatures = \u001b[0m\u001b[1mNumericRange{n_subfeatures} @ 4…31\u001b[22m,), bagging_fraction = \u001b[0m\u001b[1mNumericRange{bagging_fraction} @ 3…94\u001b[22m),\n",
       "                            minimize = true,\n",
       "                            full_report = true,)\u001b[0m\u001b[1m @ 1…88\u001b[22m"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuned_forest = TunedModel(model=forest, \n",
    "                          tuning=Grid(resolution=12),\n",
    "                          resampling=CV(nfolds=6),\n",
    "                          nested_ranges=nested_ranges, \n",
    "                          measure=rms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(model = (atom = (pruning_purity_threshold = 0.0,\n",
       "                  max_depth = -1,\n",
       "                  min_samples_leaf = 5,\n",
       "                  min_samples_split = 2,\n",
       "                  min_purity_increase = 0.0,\n",
       "                  n_subfeatures = 3,\n",
       "                  post_prune = false,),\n",
       "          weights = Float64[],\n",
       "          bagging_fraction = 0.8,\n",
       "          rng_seed = 0,\n",
       "          n = 300,\n",
       "          parallel = true,),\n",
       " tuning = (resolution = 12,\n",
       "           parallel = true,),\n",
       " resampling = (nfolds = 6,\n",
       "               parallel = true,\n",
       "               shuffle = false,),\n",
       " measure = MLJ.rms,\n",
       " operation = StatsBase.predict,\n",
       " nested_ranges = (atom = (n_subfeatures = \u001b[0m\u001b[1mNumericRange{n_subfeatures} @ 4…31\u001b[22m,),\n",
       "                  bagging_fraction = \u001b[0m\u001b[1mNumericRange{bagging_fraction} @ 3…94\u001b[22m,),\n",
       " minimize = true,\n",
       " full_report = true,)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params(tuned_forest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluating the self-tuning random forest "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We evaluate our self-tuning random forest the same as any other model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Evaluating using a holdout set. \n",
      "│ fraction_train=0.8 \n",
      "│ shuffle=false \n",
      "│ measure=Function[rms, rmslp1] \n",
      "│ operation=StatsBase.predict \n",
      "│ Resampling from all rows. \n",
      "└ @ MLJ /Users/anthony/Dropbox/Julia7/MLJ/MLJ/src/resampling.jl:91\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(MLJ.rms = 4.0594329002931975,\n",
       " MLJ.rmslp1 = 0.25803568683865385,)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mach = machine(tuned_forest, task)\n",
    "evaluate!(mach, resampling=Holdout(fraction_train=0.8), measure=[rms, rmslp1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Implicit in this evaluation is nested resampling: `evaluate!` fits `tuned_forest` on the training data and evaluates its performance on the 20% holdout test set. On the other hand, fitting `tuned_forest` means:\n",
    "(i) searching over all parameters specified by `nested_ranges` above for the values optimizing performance, as estimated by cross-validation on the supplied data (80% of all data); and (ii)\n",
    "and retraining a `forest` on *all* that data using the optimal parameters.\n",
    "\n",
    "We can view the optimal `forest` hyperparameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(best_model = \u001b[0m\u001b[1mDeterministicEnsembleModel @ 6…43\u001b[22m,)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fitted_params(mach)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best.bagging_fraction = 0.5636363636363636\n",
      "(best.atom).n_subfeatures = 7\n"
     ]
    }
   ],
   "source": [
    "best = fitted_params(mach).best_model\n",
    "@show best.bagging_fraction\n",
    "@show best.atom.n_subfeatures;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And plot the performance estimates for the grid search:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@suppress plot(mach)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Julia 1.1.0",
   "language": "julia",
   "name": "julia-1.1"
  },
  "language_info": {
   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
   "version": "1.1.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
